When a TV image is received, noises in the TV image are generally present. The noises are due to various types of interferences during a transmission process of the TV image and a discontinuous block effect resulted from image compression, with the discontinuous block effect being regarded as high frequency (HF) noises. In order to remove noises of a TV image, a common approach is to apply a low-pass filter to remove HF components of the image signal, so that a discontinuous block caused by the discontinuous block effect is smoothed. However, in a filtering process, although the noises are reduced, edges formed by connecting two groups of areas comprising different pixel luminance values are blurred due to smoothing.
In order to solve the foregoing problem, a non-linear map technology, i.e., a Gamma Map filter, is applied in the prior art, and an algorithm thereof is:
                    y        ⁡                  (          t          )                    =                                                  (                              α                -                2                            )                        *                          z              2                                +                                                                      z                  2                                *                                                      (                                          α                      -                      2                                        )                                    2                                            +                              8                ⁢                                                                  ⁢                α                *                z                *                                  x                  ⁡                                      (                    t                    )                                                                                                2          ⁢                                          ⁢          α                      ,                  ⁢    and              α      =              2        /                  (                                                    s                2                                            z                2                                      -            1                    )                      ,  where s2 represents a luminance variance of an image pixel, z represents a luminance average of the image pixel, x(t) presents an original luminance value of a pixel position t, and y(t) represents an updated luminance value of the pixel position t. A disadvantage of the Gamma Map filter is that the calculation complexity as well as the application cost is rather high.
A motion averaging technology, i.e., a Lee filter, is applied in the prior art, and an algorithm thereof is:
            y      ⁡              (        t        )              =                  β        ⁢                                  ⁢                  x          ⁡                      (            t            )                              +                        (                      1            -            β                    )                ⁢        z              ,            and      ⁢                          ⁢      β        =          max      ⁡              (                                                            s                2                            -                              s                n                2                                                    s              2                                ,          0                )              ,where s2 represents a luminance variance of an image pixel, z presents a luminance average of the image pixel, s2 represents an estimated noise variance, x(t) represents an original luminance value of a pixel position t, and y(t) represents an updated luminance value of the pixel position t. A disadvantage of the Lee filter is that not only the noise filtering effect is unsatisfactory but also edges are blurred from averaging.
Therefore, it is necessary to provide a low-cost solution capable of effectively removing noises of a TV image with a low calculation complexity while maintaining distinct edges.
In view of the foregoing problem, the present disclosure provides a novel algorithm to remove noises of a TV image, and the algorithm is capable of adaptively adjusting a weight distribution corresponding to a plurality of pixel adjacent to a target pixel of an image frame, and performing a weight calculation on luminance values of the plurality of pixels adjacent to the target pixel according to the weight distribution to generate an updated luminance value.